DOI : https://doi.org/10.18517/ijaseit.8.3.2756

Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm

Fatiha Elghibari (1), Rachid Elouahbi (2), Fatima El Khoukhi (3)
(1) Department of mathematics and computer Science, Moulay Ismail University, Faculty of Sciences, Meknes, Morocco
(2) Department of mathematics and computer Science, Moulay Ismail University, Faculty of Sciences, Meknes, Morocco
(3) Team of Applied Computer Modelling in Humanities, Moulay Ismail University, Faculty of Humanities, Meknes, Morocco
Fulltext View | Download
How to cite (IJASEIT) :
Elghibari, Fatiha, et al. “Data Mining for Detecting E-Learning Courses Anomalies: An Application of Decision Tree Algorithm”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 3, June 2018, pp. 980-7, doi:10.18517/ijaseit.8.3.2756.
Citation Format :
E-learning adaptation has become the most important method that facilitates access to the appropriate content. Adaptive approaches consist of reducing the problems of incompatibilities between learner’s cognitive abilities and educational content’s difficulties. In some cases, the adapted curriculum cannot meet learner's skills completely seen its incoherent structure, its unsuitable methodologies and sometimes its complexity. Therefore, we need to measure the convenience of the content material to improve it and ensure learners’ satisfaction. In other words, it is necessary to estimate its appropriateness to each learner. That is why; we have proceeded by using decision tree (DT) algorithm which is a supervised data mining method. It helps to predict the convenience of the proposed content material for learners. Our system consists of classifying learning material into two classes: “good” if it is convenient, and “anomaly” if not. To achieve that, we have used an intelligent agent called Classifier Agent (CLA). It tracks learner’s behavior by collecting a set of attributes like score, learning time, and number of attempts, feedback and interactions with the tutor. Then, he calculates the predictive attribute by using the (DT) algorithm. The finding algorithm shows that the score is the most crucial indicator gives us more information about the conformity of curriculum to learners, followed by learning time, feedback and number of attempts.

D. Hooshyar, R. Ahmad, M. Yousefi, F. Yusop and S.-J. Horng, “A flowchart”based intelligent tutoring system for improving problem”solving skills of novice programmers,” Journal of computer assisted learning, vol. 31, no. 4, pp. 345-361, 2015.

F. Elghibari, R. Elouahbi and F. El Khoukhi, “modí¨le d’un processus d'apprentissage adaptí© aux í©volutions cognitives du profil de l'apprenant,” in 1í¨re Edition du Workshop International sur les Approches Pí©dagogiques & E-Learning, Fez, Morocco, 2015.

R. Elouahbi, D. Bouzidi, N. Abghour and A. M. Nassir, “A flexible approach to modelling adaptive course sequencing based on graphs implemented using XLink,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 3, no. 1, pp. 1-9, 2012.

M. Ghenname, M. Abik, R. Ajhoun, J. Subercaze, C. Gravier and F. Laforest, “Personalized Recommendation Based Hashtags on E-learning Systems,” in ISKO-Maghreb 2013 Concepts and Tools for Knowledge Management (KM), Tunisia, 2013.

V. Esichaikul, S. Lamnoi and C. Bechter, “Student Modelling in Adaptive E-Learning Systems,” Knowledge Management & E-Learning: An International Journal, pp. 342-355, 2011.

S. Bateman, C. A. Brooks and G. I McCalla, “Collaborative Tagging Approaches for Ontological Metadata in Adaptive E-Learning Systems,” in Proceedings of the Fourth International Workshop on Applications of Semantic Web Technologies for E-Learning, 2006.

X. Daomin, “Multi-Agent Based e-Learning Intelligent Tutoring System for Supporting Adaptive Learning,” in Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013.

F. Elghibari, R. Elouahbi, F. El Khoukhi, L. Ezzahri and S. Amali, “the 15th International Conference onIntelligent model for measuring learners' satisfaction towards new contents,” in Information Technology Based Higher Education and Training (ITHET), Istanbul, Turkey, 2016.

I. Kamsa, F. Elghibari, R. Elouahbi, S. Chehbi and F. El Khoukhi, “Learning time planning in a distance learning system using intelligent agents,” in 14th Information Technology Based Higher Education and Training (ITHET), Lisbon, Portugal, 2015.

F. Elghibari, R. Elouahbi, F. Elkhoukhi, S. Chehbi and I. Kamsa, “Intelligent e-learning system model for maintenance of updates courses,” in 15th International Conference on Information Technology Based Higher Education and Training, Lisbon, Portugal, 2015.

A. Peña-Ayala, “Educational data mining: A survey and a data mining-based analysis of recent works,” ELSEVIER, vol. 41, no. 4, pp. 1432-1462, 2013.

G. Calas, “Studies of the main data mining algorithms,” [SCIA] EPITA, Kremlin-Bicíªtre, 2009.

Bermudes, “Decision tree,” Zeste du savoir, 2016.

A. L. Buczak and E. Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153 - 1176, 2016.

C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005,” ELSEVIER: Expert Systems with Applications, pp. 135-146, 2007.

D. Hooshyar, M. Yousefi and H. Lim, “A systematic review of data-driven approaches in the player modeling of educational games,” Artificial Intelligence Review, pp. 1-21, 2017.

D. Hooshyar, R.. B. Ahmad, M. Yousefi, M. Fathi, S.-J. Horng and H. Lim, “Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills,” Elsevier: Computers & Education, vol. 94, pp. 18-36, 2016.

C. Romero, S. Ventura, and E. Garcıa, “Data mining in course management systems: Moodle case study and tutorial,” Elsevier: Computers & Education, p. 368-384, 2008.

E. Osmanbegović and M. Suljić, “Data Mining Approach For Predicting Student Performance,” Economic Review - Journal of Economics and Business, vol. 10, no. 1, pp. 3-12, 2012.

S.-C. Cheng, Y.-M. Huang, J.-N.. Chen and Y.-T. Lin, “Automatic Leveling System for E-Learning Examination Pool Using Entropy-Based Decision Tree,” Springer-Verlag Berlin Heidelberg, p. 273 - 278, 2005.

E. í–zpolat and G. B. Akar, “Automatic detection of learning styles for an e-learning system,” ELSEVIER: Computers and Education, p. 355-367, 2009.

D. Hooshyar, M. Yousefi, and H. Lim, “Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review,” ACM Computing Surveys (CSUR), vol. 50, no. 6, p. 90, 2018.

C. F. Lin,. Y.-c. Yeh , Y. H. Hung and R. I Chang, “Data mining for providing a personalized learning path in creativity: An application of decision trees,” ELSEVIER: Computers & Education, p. 199-210, 2013.

G. Chen, C. Chang and. C. Wang, “Using adaptive e-news to improve undergraduate programming courses with the hybrid format,” ELSEVIER: Computers & Education, p. 239-251, 2008.

M. A. Al-Barrak and M. Al-Razgan, “Predicting Students Final GPA Using Decision Trees: A Case Study,” International Journal of Information and Education Technology, vol. 6, no. 7, pp. 528-533, 2016.

C. Romero, V. Sebastií¡n, P. G. Espejo and C. Herví¡s, “Data Mining Algorithms to Classify Students,” in International Conference on Educational Data MiningMontrí©al, Quí©bec, 2008.

I. Lykourentzou , I. Giannoukos, V. Nikolopoulos, G. Mpardis and V. Lou, “Dropout prediction in e-learning courses through the combination of machine learning techniques,” ELSEVIER: Computers & Education, p. 950-965, 2009.

N. Thai-Nghe, A. Busche and L. Schmidt-Thieme, “Improving Academic Performance Prediction by Dealing with Class Imbalance,” in International Conference on Intelligent Systems Design and Applications, 2009.

S. Kotsiantis, K. Patriarcheas and M. Xenos, “A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education,” ELSEVIER:Knowledge-Based Systems, p. 529-535, 2010.

A. P. Muniyandia, . R. Rajeswarib and R. Rajaramc, “Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm,” ELSEVIER: Procedia Engineering, p. 174 - 182, 2012.

I. Firdausi, C. Lim, A. Erwin and A. S. Nugroho, “Analysis Of Machine Learning Techniques Used In Behavior-Based Malware Detection,” in International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2010.

B. Qian and K. Rasheed, “Stock market prediction with multiple classifiers,” Springer Science+Business Media, pp. 26-33, 2006.

L. Rokach and O. Maimon, “Decision Trees,” in Data Mining and Knowledge Discovery Handbook, Springer, 2005, pp. pp 165-192.

V. Kumar, M. Steinbach and P.-N. Tan, “http://text.123doc.org/document/1148085-data-mining-classification-basic-concepts-decision-trees-and-model-evaluation-lecture-notes-for-chapter-4-introduction-to-data-mining-pptx.htm,” in Introduction data mining, Addison-Wesley Companion Book Site , 2005, pp. 145-204.

J.-C. Lin and K.-C. Wu, “Analyze on the Learning Behavior of E-Learning,” JDCTA (International Journal of Digital Content Technology and its Applications, vol. 4, no. 5, pp. 1-6, 2010.

E. Holohan, M. Melia, D. McMullen and C. Pahl, “Adaptive E-Learning Content Generation based on Semantic Web Technology,” in International Conference on Artificial Intelligence in Education AIED, 2005.

F. Elghibari, R. Elouahbi and F. El Khoukhi, “An Automatic Updating Process to Control The E-learning Courseware,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 2, pp. 546-551, 2017.

U. Fayyad, G. Piatetsky-Shapiro and P. . Smyth , “From Data Mining to Knowledge Discovery in Databases,” American Association for Artificial Intelligence, pp. 37-54, 1997.

D. Michie, D. J. Spiegelhalter and C. C. Taylor, “Machine Learning, Neural, and Statistical Classification,” Journal of the American Statistical Association,, vol. 91, pp. 436-438, 1996.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).